Similarity Scores Based Re-classification for Open-Set Person Re-identification

Abstract

In this paper, we propose a new similarity scores based re-classification method for open-set person re-identification, which exploits information among the top-n most similar matching candidates in the gallery set. Moreover, to make the cross-view quadratic discriminant analysis metric learning method effectively learn both the projection matrix and the metric kernel with open-set data, we introduce an additional regularization factor to adjust the covariance matrix of the obtained subspace. Our Experiments on challenging OPeRID v1.0 database show that our approach improves the Rank-1 recognition rates at 1% FAR by 8.86% and 10.51% with re-ranking, respectively.